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Routing in mobile wireless sensor network: A survey
Article in Telecommunication Systems · September 2013
DOI: 10.1007/s11235-013-9766-2
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Telecommun Syst
DOI 10.1007/s11235-013-9766-2
Routing in mobile wireless sensor network: a survey
Getsy S Sara · D. Sridharan
© Springer Science+Business Media New York 2013
Abstract The Mobile Wireless Sensor Network (MWSN)
is an emerging technology with significant applications. The
MWSN allows the sensor nodes to move freely and they are
able to communicate with each other without the need for
a fixed infrastructure. These networks are capable of outperforming static wireless sensor networks as they tend to
increase the network lifetime, reduce the power consumption, provide more channel capacity and perform better targeting. Usually routing process in a mobile network is very
complex and it becomes even more complicated in MWSN
as the sensor nodes are low power, cost effective mobile devices with minimum resources. Recent research works have
led to the design of many efficient routing protocols for
MWSN but still there are many unresolved problems like retaining the network connectivity, reducing the energy cost,
maintaining adequate sensing coverage etc. This paper addresses the various issues in routing and presents the state
of the art routing protocols in MWSN. The routing protocols are categorized based on their network structure, state
of information, energy efficiency and mobility. The classification presented here summarizes the main features of many
published proposals in the literature for efficient routing in
MWSN and also gives an insight into the enhancements that
can be done to improve the existing routing protocols.
G.S Sara (B) · D. Sridharan
Department of Electronics & Communication Engineering,
College of Engineering, Anna University,
Guindy Chennai 600 025, India
e-mail: getsysudhir@gmail.com
G.S Sara
Shalom, 13&14B, Adinaath Avenue, Santhoshapuram, Chennai,
Tamil Nadu 600 073, India
e-mail: getsysudhir@hotmail.com
Keywords Survey · Mobile wireless sensor network ·
Routing · Mobility and energy efficiency
1 Introduction
A mobile wireless sensor network consists of sensor nodes
that have the ability to move within the network [101].
A sensor node is a tiny device that includes three basic components: a sensing subsystem for data acquisition from the
physical surrounding environment, a processing subsystem
for local data processing and storage and a wireless communication subsystem for data transmission [28, 118]. Preliminary studies show that introducing mobility in wireless sensor network is advantageous [73, 79, 86, 114, 122]. Mobility
can be achieved by equipping the sensor nodes with mobilizers for changing their locations [28] or the sensors can
be made to self propel via springs [12, 123] or wheels [15]
or they can be attached to transporters like vehicles, animals, robots [65] etc. Sometimes the sensor nodes may
move due to the environment (ocean or air) in which they
are placed [100]. The recent year researches prove that mobile wireless sensor networks outperform the static wireless sensor networks as they offer the following advantages
[8, 28, 101, 123]:
• A sparse architecture may be considered for a mobile sensor network design
• MWSN has a dynamic topology which reflects in the
choice of other characteristic properties such as routing,
MAC level protocols and physical characteristics
• In static WSN, an initially connected network can turn
into a set of disconnected subnet works due to hardware
failure or energy depletion but in MWSN, the nodes can
be used to reorganize the network
G.S Sara, D. Sridharan
Fig. 1 Mobile Wireless Sensor
Network
• The lifetime of a sensor network can be increased using
mobile sensor nodes [67, 92]
• Mobile sensors can relocate after initial deployment to
achieve the desired density requirement and to reduce the
energy holes in the network
• Mobility can reduce energy consumption during communication [92]
• MWSN has more channel capacity as compared to static
WSN [7]
• Better targeting can be achieved using MWSN
• Data fidelity can be achieved by MWSN by reducing the
number of hops owing to which the probability of error
decreases
Figure 1 explains the architecture of three tier Mobile
Wireless Sensor Network [101]. The sensor nodes are deployed randomly in the network. These nodes can communicate with each other and the mobile agents. The mobile
agents can move anywhere, at any time and they are responsible for collecting sensed data and forwarding them to the
fixed network consisting of Access Points.
A few of the applications where mobile wireless sensor
network can be employed include a group of ornithologists
monitoring the ecology of migratory birds, a mobile worker
(might be a robot or human) equipped with sensor(s) collecting and transmitting data to the sink about agriculture
production, smart city, e-voting, intelligent traffic system,
firefighters moving through a burning building etc. [60, 101,
124, 128]. Some of the applications may need the support of
mobile sinks such as soldiers equipped with Personal Digital
Assistants (PDA) moving in battlefield for enemy detection
and a rescuer who is equipped with PDA moving in a disaster area searching for survivors.
But introducing mobility in a wireless sensor network is
very challenging as path breakage happens frequently due to
channel fading, shadowing, interference, node mobility and
node failure. Preconstruction of message delivery networks
will not be of much help here as the topology changes too
frequently. Frequent location-updates from a mobile node
can lead to excessive drain of the sensor node’s battery supply and also can increase collisions [75]. Owing to these,
factors like mobility of nodes, bandwidth restrictions and
limited resources etc., have to be considered in designing
MWSN. Based on the type of communication, two kinds of
MWSN exist [101]:
Routing in mobile wireless sensor network: a survey
Infrastructure Network—A mobile unit is connected with
the nearest base station that is within its communication radius to contact
Infrastructure less Network—In this type of network, no
fixed router is needed and all mobile units are capable of
movement. They are self organizing with the capability to
establish communication in an arbitrary manner.
Routing is the act of moving a data packet from source
to destination. The route of each message destined to the
base station in a sensor network is crucial in terms of network life time. Long routes can increase the network delay
while routing via the shortest route can always cause the intermediate nodes to drain the energy supply soon, leading
to network partitioning. The best routing protocol is the one
that covers all states of a specified network and never consumes too much of network resources. Sensor nodes have
limited energy supply and minimizing the power consumption is crucial in Mobile Wireless Sensor Network.
Mobility in tandem with energy efficiency in a wireless
sensor network endows with significant challenges for routing [25, 48, 58, 95].
• It is impossible to build a global addressing scheme for
the deployment of a large number of sensor nodes as it
leads to an increase in overhead maintenance
• The highly dynamic nature of MWSN and the frequent
change in network topology and communication links,
due to node mobility or link faults, make routing a real
challenge [37]
• Energy processing and storage capacities of mobile sensor nodes are limited. Due to the dynamic nature of the
network topology, various nodes will deplete their energy
supplies and drop out of the network leading to the partitioning of the network
• Multiple sensors may sense the occurrence of an event simultaneously and generate data. This data traffic has to be
aggregated to improve the energy and bandwidth utilization
• Sensor networks are usually application specific. The design requirements of a MWSN change with application
• Knowing the position of mobile sensor nodes is important
since data collection is based on location. Routing protocol can be designed to aid this
• The routing protocol should be adaptable to the self organizing nature of the nodes
• Dynamic clustering architecture should be tailored by the
routing protocol as depletion of power from cluster head
can be prevented; thereby extending the network’s lifetime
• Randomized path choice should be imbibed as multiple
paths to a destination with low overheads can help in balancing the network load and tolerating the failure of nodes
• Good thresholds can be set for sensor nodes for energy,
time delay and to transfer the sensed data. This would
save energy by limiting unnecessary transmissions
1.1 Routing protocols
Several routing protocols are proposed by many researchers,
based on various criteria and design issues. No routing protocol can be termed as perfect, as each routing protocol may
be suitable for some application but may have loopholes
when judged in some other perspective. The routing protocols of MWSN can be mainly classified based on their
network structure, state of information, mobility and energy
efficiency techniques (Fig. 2).
• Based on the network structure, they are further cataloged
as Direct Communication Routing, Flat based Routing,
and Hierarchical routing. In direct communication routing, a sensor node sends data directly to sink. The power
of the sensor node drains very quickly here if the network
area is large and the number of collision too increases.
Therefore direct communication routing is hardly used in
mobile wireless sensor network. The flat based routing
protocol assign the same functionality to all nodes [48].
It is very simple and efficient for small networks. It is
further categorized into Opportunistic Routing (OR) and
Best Path routing [131]. The idea behind opportunistic
routing is that for each destination, a set of next hop candidates are selected and each of them is assigned a priority according to its closeness to the destination. When
a packet needs to be forwarded, the highest priority node
is chosen as the next hop. The best path routing scheme
attempts to find the best path and forwards packets to the
corresponding next hop. The Hierarchical protocols dynamically organize the nodes in the network into partitions called clusters and the clusters are further aggregated into larger partitions called super clusters and so
on. The cluster heads aggregate the data; thereby reducing
the data and saving energy [120]. They are further categorized as flat hierarchy, cluster based hierarchy and zone
based hierarchy. In flat based hierarchy all nodes have
same capabilities but different responsibilities. In cluster
based hierarchy, the physical network is transformed into
a virtual network of interconnected clusters. Each cluster
has cluster heads which make control decisions for cluster
members. The zone based hierarchy increases the scalability by shrinking the topology reorganization scope.
Zones are created and the flat scheme is applied to each
zone [51].
• Based on the state of information, the routing protocols
are grouped into topology based routing and location
based routing. The topology based routing protocols use
the principle that every node in the network maintains
large scale topology information [94]. They can be again
classified as proactive routing, reactive routing and hybrid
routing. Proactive routing also known as pre-computed
routing or table driven routing, calculates the route to
all destinations apriori and stores the information about
G.S Sara, D. Sridharan
Fig. 2 Taxonomy of routing protocols in MWSN
the links and network topology changes in a routing table. The nodes here periodically update their routing tables. Reactive routing or on demand routing computes the
route to a destination only when it is needed using route
discovery process and route maintenance. Hybrid routing utilizes the functionality of both proactive and reactive routing. Location based routing protocols make use
of position information of nodes to route data. They are
yet again ordered under the location updates as time based
location update routing protocol, distance based location
update routing protocol and predictive distance based location update routing protocol [51]. In the time based location update scheme, each node periodically sends a location update to a location server. In distance based update scheme, each node tracks the distance it has moved
since its last update and sends its location update when-
ever the distance exceeds a certain threshold. In the predictive distance based, also called as dead reckoning, the
node reports to the location server both its position and
velocity. Based on this information and the mobility pattern, the location of the node can be predicted.
• Energy efficient routing protocols in MWSN can be
broadly categorized based on when the energy optimization is performed. A mobile sensor node consumes its
battery energy not only when it actively sends or receives
packets but also when it stays idle listening to the wireless medium for any possible communication requests
from other nodes. Thus, energy efficient routing protocols must minimize either the active communication energy required to transmit and receive data packets or the
energy during inactive periods. For protocols that belong
to the former category, the active communication energy
Routing in mobile wireless sensor network: a survey
can be reduced by adjusting each node’s radio power just
enough to reach the receiving node but not more than that.
For protocols that belong to the latter category, power
saving approach can be used. Each node can save the
inactive energy by switching its mode of operation into
sleep/power-down mode or simply turn it off when there
is no data to transmit or receive. This leads to considerable
energy saving, especially when the network environment
is characterized with low duty cycle of communication
activities. However, it requires well-designed routing protocol to guarantee data delivery even if most of the nodes
sleep and do not forward packets for other nodes. Another
important approach to optimize active communication energy is load distribution. While the primary focus of the
above two approaches is to minimize energy consumption
of individual nodes, the main goal of the load distribution
method is to balance the energy usage among the nodes
and to maximize the network lifetime by avoiding overutilized nodes when selecting a routing path. Energy efficient design is a new area of research which investigates
the approaches to save battery life [26].
• Depending on the applications, the nodes that have to be
mobile are decided. The routing protocols should support
the mobility management accordingly. Based on the impact of mobility on nodes in the network, the routing algorithms are cataloged as
• Routing only when the sink is mobile
• Routing when a few nodes act as mobile relays
• Routing when all the nodes are mobile
• Routing when a few nodes are stationary
• Biologically cooperative routing is nowadays being widely
tested on MWSN and found to have remarkable adaptivity, reliability and robustness in Mobile Wireless Sensor
Network. These include nature inspired techniques like
Ant Colony Optimization, Bee Colony Optimization. Cellular Automata, Genetic algorithms etc. to find the optimal path for routing.
2 Classification based on network structure
2.1 Flat based routing protocols
When a flat based routing protocol is applied, all the sensor
nodes in the network are treated equally. They are mainly
data centric routing protocols. The nodes collaborate to perform the routing task by sending queries to certain regions
and collecting data from the sensors located in that region.
The attribute based naming is usually used here to stipulate
the properties of data. In static sensor network many routing protocols are flat-based namely, SPIN [35] and Directed
Diffusion [42] etc. They try to save energy through negotiation and elimination of redundant data [48]. But due to the
high mobility of nodes, they can’t be used in MWSN as the
link failure is very high.
2.2 Opportunistic routing
It selects a set of next hop neighbors and assigns a priority based on certain characteristics. It is mainly a post decided routing. It may exploit the broadcast nature of wireless
transmissions and dynamically selects a next hop per packet
based on loss conditions. It reduces the number of transmissions needed for reliable delivery of a packet as it avoids retransmissions as long as the packet makes progress towards
the destination. But there is jeopardy of duplicate forwarding by multiple candidates unaware of others’ transmissions.
Hence the opportunistic routing performs well only if the
highest priority candidate that received the packet forwards
it [131].
Guangcheng and Xiaodong et al. [31] suggests an opportunistic routing for mobile wireless sensor networks based
on Received Signal Strength Indicator (RSSI). The Opportunistic Probability (OP) based on the RSSI of the sink’s
beacon packets and Mobility Vector (mv) is established and
the best node with the highest OP available at that instant
is used to store and relay packets at each hop, after packets
are broadcasted. The sink sends out beacon packets periodically with higher power. The other nodes establish and update their OP value with beacon packets’ RSSI information
using the equation
(1)
OPis = OPis + 1/|RSSI| × α + mvi × c
where OPis —Updated OP value; OPis —Current OP value;
α—constant; |RSSI|—RSSI absolute value of the beacon
packet which was just received; c—Positive constant; mvi —
node i’s mobility vector.
The node that has to send the data, broadcasts packets which include OPis value to the sink in its header. The
nodes in the Forward Candidate Set examine the header of
every successfully decoded packet. If the OP value in the
packet header is lower than its OP, the node buffers and forwards the packet. The success delivery ratio of OR-RSSI
was found to be three times better than Tiny AODV [29] as
it is not based on the existing path and has more opportunity to deliver packets successfully. But the delay incurred
by OR-RSSI is observed to be very high.
Lian et al. [68] have proposed a Receiver based Opportunistic Forwarding Protocol (ROF) for MWSN that does
not need to establish global routing between source node
and sink but allows the neighbor nodes of the sender to contend for the forwarding right under certain conditions and
permits only the contention winner to forward the data. The
forwarding right is calculated based on the distance to sink,
sink node’s extra coverage area and the node’s residual energy. Nodes which obtain the forwarding right, forwards the
data packets while others discard the packets. Forwarding
delay and communication consumption of ROF is low when
the number of nodes is small and is moderate when there is
G.S Sara, D. Sridharan
Fig. 3 Kalman filter and state
predictor at mobile sink
an increase of nodes. It is less influenced by the nodes’ speed
because ROF shortens the time of forwarding right contention through dual channel communication mechanism.
The node’s residual energy is considered when calculating the forwarding priority in ROF and so it balances energy
utilization better. This protocol brings large forwarding delay in low density network.
Andrea et al. [2] suggested an opportunistic routing protocol based on estimation of mobility of sink. Here the data
packets are forwarded from a static information source to
mobile sink through a multihop WSN. While the source
and the sensor nodes are located at fixed positions, the mobile sink estimates and tracks its state using Kalman filter
(Fig. 3) [2]. Mobile sink transmits a STATE UPDATE message containing the current estimate of its state to all the
nodes. Nodes in the sensor set {Si } that successfully decode
this packet enter into a distributed contention procedure to
select the node that has to forward the information from the
sender to the sink. Basic geographic routing is performed
to deliver data. Mobility Prediction Routing (MPR) saves
energy by triggering STATE UPDATE message only when
needed. It minimizes the traffic required to reliably track the
sink by matching the frequency of STATE UPDATE transmission to the actual movement pattern of the sink. MPR
offers high reliability even when the network has to support
a high level of traffic and is able to deliver information much
quicker. So this protocol is very suitable for applications that
have high constraints in terms of latency. Higher value of
sink acceleration induces low packet delivery ratio.
The algorithm by Branislav et al. [6] is based on information potentials which can be adapted using a simple iterative distributed computation. The mobility graph is used
to encode knowledge about likely mobility pattern within
the network. It is extracted from training data and is used
to predict future relay nodes for the mobile node. Predictive
routing scheme is implemented here to optimize data delivery in sensor network. The simulation results show that the
quality of service achieved is significantly higher even if the
quality of prediction is unrealistically low. Its performance
degrades to the original routing algorithm in case of wrong
prediction, as the predicted gradient value is discarded and
the existing gradient field is adapted to the new relay node if
the predicted node is different from the actual node.
2.3 Best path routing
These types of routing protocols try to identify the best path
between source and destination using some metrics and forward packets to the next hop. It triggers many packet retransmissions and path discoveries. Le et al. [64] proposed the
PAGER-M algorithm which utilizes the location information
of the sensor node and the base station to assign each sensor
node a cost, which is close to a sensor’s Euclidean length of
the shortest path to the base station. A packet is forwarded
to the base station using greedy forwarding whenever possible. The cost for each sensor is assigned using shadow
spread phase and cost spread phase. This helps to reduce the
transmission failures caused by mobility. It is observed that
PAGER-M achieves an average delivery ratio >99 % with
beacon interval 3–4 seconds. When the number of nodes
are increased, the average delivery ratio of PAGER-M is
higher than AODV [93] routing protocol. This is because
the path length of AODV is one hop more than PAGER-M.
Due to the conservative choice of forwarding destinations in
PAGER-M, it has an average path length. The sending node
chooses the closest and safest neighbor. Due to the long beacon broadcast interval of PAGER-M, the routing overhead is
significantly lower.
Kihun et al. [59] have explained the Location based Energy Efficient Intersection Routing protocol (EELIR). During the start of the advertisement phase, the routing range
of a node is limited by the transmitting node. It is done by
forming a segment and intersection of two circles. The segment is the minimum distance from node to sink. The first
Routing in mobile wireless sensor network: a survey
circle has the node as center with the radius r equal to the
node’s maximum transmission distance. The second center
is any point that lies in the segment. The origins of two circles are discovered and intersection of the two circles form
limited routing space. The sender node ‘A’ transmits advertisement message to neighbor nodes. In the conditional reply
phase, the neighboring nodes of ‘A’ calculate whether to reply or not to the advertisement message using
(2)
d1 = (xz − xa )2 + (yz − ya )2
d2 = (xz − xb )2 + (yz − yb )2
(3)
while others play active role like traffic relaying, neighborhood management etc. [51]. Instead of transmitting a data
directly to the sink, all the nodes transmit their data to the respective cluster heads also called as aggregators [105]. The
cluster head performs data aggregation and removes redundant information. The main objective of this type of routing
is to achieve energy efficiency. It also helps to reduce the
organization complexity overhead of the network which is
proportional to the number of nodes in the network.
where location of source node (xa , ya ), location of neighbor
node (xb , yb ).
If d1 < r & d2 < r: true, then the nodes will transmit
a reply message which contains the energy level information and minimum distance of the node to the sink. During
the route selection phase, node A estimates the next node
from the received reply messages and chooses the best route
to destination. Simulation results show that nodes transmit
data to sink without flooding. So delivery ratio is increased
when compared with other flooding based routing and location aided routing but the element of delay is induced.
Therefore more research is needed to reduce the delay time.
Anycast based hybrid routing protocol [57] is primarily
an AODV based routing with major modifications to support
anycasting, distributed regions and the ability to accommodate multiple path cost metrics. Route discovery is initiated
with the expanding ring RREQ mechanism. Any node with
active sink information can generate RREP and data transmission is done using the best path. Link Layer Notification
is used to detect link failures. Due to the removal of certain
intermediate nodes, the delivery rate of Anycast AODV is
better than AODV [93] routing protocol. Classical AODV
tries to find a route to a single sink, thus losing more data as
compared to Anycast AODV.
In flat hierarchy, all the nodes in the network have the same
potential but they have different responsibilities. The Robust
cooperative Routing Protocol (RRP) proposed by Xiaoxia et
al. [117] consists of multiple nodes with the same packet
attempting to deliver it to another node cooperatively. The
authors have assumed that all nodes have the same transmission range and a path has already been established between a
source and a destination. This is called intended path. Nodes
on intended paths are called the intended nodes. A guard
node is at least a neighboring node of two intended nodes.
When an intended node fails to receive a packet from its intended upstream node, guard nodes who have successfully
received the packet by using the Wireless Broadcast Advantage (WBA) will forward the packet to the downstream
nodes without waiting for the routing instruction. The packet
is delivered either to the intended downstream node if reachable or to the node that lost the packet. Guard links can
improve the reliability and reduce the end to end delay at
the cost of spending more energy in overhearing at guard
nodes. Traditional alternative routing methods have to wait
for the timeout at the network layer and then find the alternative path to replace the failed path but RRP can forward
the packet at the MAC layer and hence reduces the transfer
delay at the intermediate nodes on the path. RRP outperforms Destination Sequence Distance Vector (DSDV) [11]
and Adhoc On Demand Multipath Distance Vector Routing
(AOMDV) [76] in terms of packet delivery ratio due to its responsiveness to topology changes and as the robust path bear
implicit geographic information about intended path. They
can react quickly to link failure through cooperation. End to
end delay of RRP increases with link error probability because of longer latency for selecting an available path and
more retransmissions. Instead of relying on retransmission
at MAC layer and searching for new paths, RRP delivers the
packet over the most reliable path located in the robust path.
M-Geocast proposed by Lynn et al. [75] is a robust and
energy efficient geometric routing protocol with multiple
mobile sinks. One of the mobile sinks is chosen as the master sink and it acts as location service provider, data collector
and dissemination server. Simple geographic routing is used
2.4 Hierarchical routing
Flat network architecture will not be apposite if the network
size is large as the sink gets overloaded when the number of
nodes is increased. This becomes a bottleneck, causing delay in communication owing to which there may be a chance
of packet loss. Single gateway architecture is not scalable
for a larger set of sensors covering a wider area of interest, since the sensors are typically not capable of long haul
communication [58]. Network clustering can provide solution to these problems. The nodes in the network are dynamically organized into clusters based on certain parameters like distance, residual energy etc. A hierarchy in sensor
nodes is created when a subset of nodes have more responsibilities than other nodes in the network. In hierarchical routing, some nodes play a passive role like listening to traffic
2.4.1 Flat hierarchy
G.S Sara, D. Sridharan
Fig. 4 Three layer mobile node
architecture
by all nodes to send message to the master sink. Two optimization techniques namely, path history projection and geographic void prediction are applied in this algorithm. Simulation analysis has shown that M-Geocast successfully delivers more than 99 % of all events even as sinks increase.
Though the node speed is increased, delay incurred by MGeocast remains stable because geometric routing does not
incur additional overhead regardless of its speed, as long as
the location information of the destination remains the same.
2.4.2 Cluster hierarchy
In this architecture, the sensors organize themselves into
clusters and each cluster has a cluster head. The cluster
heads process, aggregate and forward the information via
other cluster heads to the base station [62]. The nodes’ battery life is perked up; thereby improving the network lifetime but the cluster head sometimes becomes the bottle neck
since all communications pass through it [51].
Zhi-Feng et al. [130] have designed three layered mobile
node architecture to organize all the sensors in the mobile
wireless sensor network. In order to reduce the complexity of the sensor network, the data collection, routing table
maintenance and data processing responsibilities are placed
on different set of nodes. There are three types of sensor
nodes performing different functions and having different
capabilities (Fig. 4). The Sensor nodes or S nodes have limited storage and processing capacity. They can’t communicate with each other and can move randomly with wind and
water. They can send data to adjacent Fusion node (F node)
within one hop. F nodes are in charge of maintaining routing
table, receiving and fusing data from S nodes and transmitting data to Control nodes (C nodes) by the shortest path.
The C nodes act as data warehouse, gateways and connect to
internet. The shortest path routing protocol based on Floyd
algorithm [99] is used by the F nodes to perform the routing.
Shortest Path (SP) avoids frequent cluster head election and
saves large amount of energy.
It is seen that nodes in LEACH [36] die much quickly
than nodes in SP. Blind spots are reduced using SP. Lifetime
of nodes using SP routing protocol is increased 13 times
when compared with LEACH.
Lan et al. [63] have modified the LEACH routing protocol [36] to support mobility of nodes. The sensing area is
divided into sub areas and location of cluster heads is optimized. Let n be the number of sensors in a given sub area
where node i has x–y coordinate and the distance to cluster
head is xi , yi , di .
j
Ci is the cost function for the node i to be cluster head
node of cluster j .
2 2
j
∗
Ci = vi × xi − xjc − yi − yjc
(4)
vi∗ = vt
if vi < vt ;
else vi∗ = vi
where vi is the velocity of node i, vt is the threshold velocity, xjc and yjc are the optimal location for the cluster head
j
in the sub area. The node with the smallest Ci is chosen
Routing in mobile wireless sensor network: a survey
to be the cluster head of cluster j . The calculation is done
by base station and broadcasted to all nodes. After receiving data from sensor nodes, the cluster head aggregates the
data and sends the processed data back to base station. MLEACH increases both the network life time and number
of data packets received at the base station. Data delivery
is about 8 % better than LEACH when dealing with node
mobility.
routing is employed which uses the Energy Aware Selection
Mechanism (EA) and Maximal Nodal surplus Energy estimation technique. The hybrid routing concept employed in
this algorithm helps to reduce the wastage of bandwidth and
control overhead. It reduces the control traffic produced by
periodic flooding of routing information as seen in proactive
routing [90].
2.4.4 Grid based routing
2.4.3 Zone hierarchy
It is an extension to the flat scheme. The network is divided
into different zones. By shrinking the topology reorganization scope, the scalability can be increased. This is achieved
as each zone performs distributive routing. Some protocols
are able to create non overlapping zones while others are not.
It reduces the reorganization complexity inferred by node’s
movement [51].
Cluster based Routing Protocol for Mobile Sensor Networks was proposed by Liliana et al. [71]. It designs a routing protocol for high density wireless sensor networks where
the clusters are formed based on the mobility patterns of
sensors but the overhead caused by the mobility information is not very high. All the sensor nodes are assumed to
be homogeneous and they are location aware. The base station is assumed to be stationary. The sensor field is divided
into different square zones. Each zone has a unique zone
ID corresponding to the coordinates of the origin point of
the area it covers in the plane. The zone size determines the
neighboring nodes of a sensor node. Each zone has a zone
head that acts as the gateway between sensors in the cluster
and the base stations or other cluster heads. The routing is
done only by the zone head. The destination node collects
several paths when it receives Route Request (RREQ) messages from the source node in a period of time and selects
the most stable path and sends Route Reply (RREP) messages back along the path. This routing protocol can assure
better routing stability.
Getsy et al. [27] proposed a multipath hybrid routing that
can be designed for dynamic energy deficient mobile wireless sensor network where energy dissipation reduction and
reliable transmission of data is a must. Despite the real shape
of the sensor field, the entire area is circumscribed into a big
square and then divided into different zones called precincts.
Each zone consists of a head node called fusion node or
precinct head which is selected based on the surplus energy of the node. Every node within a precinct communicates with the precinct node using single hop communication. When an event is detected the sensor node first communicates with the fusion node. The fusion node checks if
the destination is within its precinct. If so, proactively the
event is sent to the destination. This is called as intra precinct
routing. To forward the data to other precincts, inter Precinct
In the proposed protocol by Jae Min Choi et al. [46], a grid
is constructed in the sensor field. All the sensors store their
own location information and related grid ID through GPS.
A cluster is formed on the basis of grid ID. Initially the cluster heads are selected randomly. The mobile sink selects four
of the nearest neighbor cluster heads at the maxim to form
the Agent Cluster head (ACH). When an event takes place in
a sensor field, the source detects the event, constructs the announcement packet and transmits it to the cluster heads that
the source belongs to. The cluster head does the data merging of the announcement packet and sends the data in the direction of the stored ACH. The ACH forwards the packet by
means of location information to the mobile sink. The proposed protocol has reduced the number of control packets
because it has one grid construction and cluster configuration.
Grid Based Energy Efficient Routing (GBEER) [60] addresses the problem of packet transmission from multiple
sources to multiple mobile sinks in large scale sensor network. The sensing field is divided into grid structure (Fig. 5)
and sensor nodes decide their cells based on the location information and the header is selected randomly. To advertise
the data detected by a sensor node, the header sends data announcement packet to other headers. In order to efficiently
advertise and request the data, the header employs the concept of quorum [33, 34]. The sensor node which detects
an event becomes the source and it generates a Data Announcement (DA) packet and sends it to its cell header using
greedy geographical forwarding [5]. The header aggregates
the data and compresses these packets. Then it forwards the
DA packet through the announcement quorum to which it
belongs. While propagating the DA packet through the announcement quorum, each header stores the packet forwarding information. The simulation results show that GBEER
is not affected by speed of sinks. The Two Tier Data Dissemination model (TTDD) [20] has slightly higher average
delivery success ratio than GBEER.
2.5 Discussion
The simplicity of flat routing protocol makes it an attractive choice of routing for small networks. As the network
becomes large, route hop count increases, link breakage
G.S Sara, D. Sridharan
Fig. 5 Data announcement and
data request in GBEER
happens frequently and end to end delay increases, it will
not be able to support high mobility as the link failure becomes very severe due to mobility. This routing technique
introduces heavy overhead which consumes more network
capacity. Occasionally routing information about remote
nodes becomes inaccurate due to long transmission time required by flat routing technique [70].
The Opportunistic Routing (OR) can exploit the existence of many good candidates for making forward progress.
It performs better when the loss probability is high and
therefore this type of routing is more suitable at high data
rates. On using this routing, the success delivery ratio is
higher as compared to best path routing since OR is not
based on existing path and it has more opportunity to deliver
packets successfully. Receiver based opportunistic routing
performs well in terms of communication and storage cost.
The OR routing achieves better robustness against sink mobility because it adapts the beaconing interval to the mobility
pattern of the sink. The OR offers high reliability even when
the network has to support a high level traffic and is able to
deliver information to multiple mobile sinks much quicker
which makes it more suitable for applications that have high
constraints in terms of latency [2]. But when the network
density is low, the OR does not perform well at high transmission rates due to the reduction in the number of links in
the network as there will not be enough good candidates for
opportunistic forwarding [131].
Simulation analysis done by Guangcheng and Xiaodong
[31] prove that the number of control packets can be reduced
on using best path routing. They reduce a bulk of routing
overhead if the beacon broadcast interval is long [64]. It
is also observed that when the number of source or sink is
varied, the best path routing’s latency becomes substantially
higher because RREQ flooding required here to find the best
path generates significant traffic subsequently inducing additional contention. The average message latency gradually
increases here as the node speed increases because the route
entries cached by this type of routing become no longer
valid [75]. So the best path routing is not always an optimum solution for MWSN. On the other hand opportunistic
routing with priority based contention forwarding and dual
channel mechanism promises a better routing protocol for
MWSN with higher data rates and lesser collisions as Priority based routing decreases the control packet and communication cost and also as the time required for forwarding right
contention can be shortened using dual channel mechanism
to OR [68].
One can achieve a comparatively stable network topology by systemizing the network into clusters or groups [83,
120]. To an extent, the dynamic property of the network
topology can be limited to a cluster using hierarchy routing.
Routing in mobile wireless sensor network: a survey
Table 1 Pros and Cons of Flat
based Routing and Hierarchical
Routing in MWSN
Routing methodology
Advantages
Disadvantages
Flat Based Routing
• Good for small networks
• Opportunistic routing
can achieve better
robustness against sink
mobility.
• Offers high reliability
• OR can be used for
applications with high
delay constraints
• Best path routing helps to
reduce control packets
• Does not support high
mobility
• In low density network
OR does not perform
well at high transmission
rates
• Latency of best path
routing increases as
node speed increases
Hierarchical Routing
• Reduces unnecessary
routing packets
• Hybrid routing reduces
wastage of bandwidth,
minimizes control traffic
and decreases collision
• Complexity occurs due
to selection and
maintenance of cluster
heads
Only the stable and high level information are mainly propagated across a long distance to avoid unnecessary routing
overhead [54, 83]. A hierarchical cluster can be deployed
to achieve some kind of resource reuse such as frequency
reuse and code reuse [44, 54, 120]. It also helps to achieve
reduction in interference by using different spreading codes
across clusters [44]. The data fusion and aggregation employed in hierarchical routing helps to reduce energy consumption within the network. The overhead and complexity
in this type of routing protocol comes from the selection and
maintenance of cluster head [70]. Hybrid routing usually
adopted in zone routing reduces the wastage of bandwidth.
Periodic flooding of routing information to the sink is reduced here, which in turn reduces the control traffic [27, 90].
The scope of future research using hierarchical routing for
MWSN lies in the design of a hybrid routing protocol that
incorporates frequency reuse or code reuse modus operandi
along with simple and efficient data aggregation technique.
Table 1 summarizes the major advantages and disadvantages
of flat based routing and hierarchical routing in MWSN.
3 Classification based on state of information
3.1 Topology based routing
The topology refers to the network layout or network shape.
It is defined as the set of communication links between
node pairs used explicitly or implicitly by routing mechanism [97]. A proper topology control algorithm is needed to
improve network lifetime, reduce interference, increase network capacity and utilization, reduce end to end delays and
increase the robustness to frequent node failures [89]. Topology based routing involves a hop wise route creation from
source to destination. Topology control finds its justification
either in proactive protocols by reducing the periodic updates of their routing tables or in broadcasting protocols by
using hierarchical routing methods [51]. It tries to minimize
the broadcasting overhead and power to reach all nodes in
the network. But the complexity of this routing occurs due
to the movement of the nodes which destabilizes the network
thereby increasing route repair and route error. The topology
based routing tries to minimize the number of links between
nodes seeing to it that there is no hindrance to the network
connectivity and also minimizes the power needed for transmissions. If a node creates and maintains the topology, then
the algorithm belongs to Centralized Topology Control. In
routing algorithms, if a subset of nodes create and maintain
the topology, then they belong to Decentralized Topology
control [23].
3.1.1 Proactive routing
Routing protocols that store information in a routing table
even before it is needed are called proactive routing protocols or table driven routing protocols. They keep a track of
routes for all destinations in the network. They experience
minimum initial delay as a route can be immediately obtained from the routing table.
The CEER [110] is a hop count based routing approach.
It utilizes the color theory based dynamic localization algorithm. The network model consists of four anchors that
collect and aggregate data received from cluster heads. Each
anchor floods its RGB values and average hop distance to
each sensor node periodically so that each sensor node can
calculate its hop count to the anchor and adjust its RGB values based on the hop count. During data transmission, the
cluster head selects the one hop neighbor that is closer to a
nearby anchor than itself as the next possible hop by comparing the RGB values. This continues till the data reaches
G.S Sara, D. Sridharan
the server via the anchor. In OR-RSSI [31] powerful beacon
packets which are periodically sent by the sink are utilized
by the other nodes to establish opportunistic probability values (OP). Once the packets are broadcast by the source, the
best node with the highest OP available at that instant is used
to store and relay packets at each hop.
In [6], the authors use the information potential based
routing [72] to deliver messages from any node in the network to the sink. Each node communicates to its 1-hop
neighbors to compute and maintain the information potential. The Gauss Seidel iterative [91] method is used in which
each node holds its own potential values and periodically updates it by using the current average value of its neighbors.
3.1.2 Reactive routing
Reactive or On Demand routing protocols acquire the routing information only when it is needed. They consume less
bandwidth for maintaining the route tables at each node.
The latency for many applications will drastically increase.
In [60], the sensor node which detects an event, generates
the data announcement packet and sends it to the cell header.
The header propagates the Data Announcement (DA) packet
through the announcement quorum. The sink sends the Data
Request (DR). When the header with the DA-packet receives
the DR packet, it retrieves the DA packet and checks the data
generation time to decide whether the data is stale or not. If
the data is valid, the header sends the DR-Pkt to the source’s
header which in turn forwards the DR-Pkt to the source. The
source generates the data packet and forwards it to the sink.
3.1.3 Hybrid routing
Hybrid routing protocol limits the scope of proactive procedure to the node’s local neighborhood, but the search
throughout the network, although global, is done by querying only a subset of the network nodes. The network is divided into clusters or zones. Within a cluster region, the routing protocol continuously evaluates the route so that when
a packet needs to be forwarded, the route is already known
and can be used immediately. Between clusters or zones, the
communication is via reactive routing [10].
In Anycast based Lightweight routing protocol [57], the
sink advertises a HELLO message periodically and the
nodes receiving this cache the information with a time
stamp. On an event trigger, the nodes first check their cache
for any available sink. If no sink is available, route discovery
is initiated with the expanding ring RREQ mechanism. The
nodes with the active sink information generate RREP and
data transmission is started. Energy Efficient Mobile Wireless Sensor network [27] is a multipath hybrid routing protocol. During intra precinct routing, the nodes within a zone
communicate with the fusion head periodically and their information is stored in a routing table at the fusion node. For
inter precinct routing, the fusion nodes enables the selection
of best paths from the computation of maximal nodal surplus energy.
3.2 Location based routing
Routing protocols that deliver packets to nodes based on
their geographic locations are the Location based routing
protocols. They assume that all nodes in their network are
aware of their geographic locations. The routing destination
is specified either as a node with a given location or as a
geographic region. Each packet holds a bounded amount of
additional routing information to record where it has been in
the network [21]. There are various criteria which assure that
the location information of sensor nodes plays a very important role for routing. For example, location awareness becomes crucial to calculate the distance between two particular nodes so that energy consumption can be estimated. The
query can be directly diffused to a particular region which
in turn reduces the number of transmissions [58]. The sensor nodes can be addressed by means of their locations etc.
3.2.1 Time based
Lynn et al. [75] have designed a time based location update
routing called M-Geocast (Fig. 6). It marks each packet with
the location information of its destination. The forwarding
node makes a locally optimal greedy choice by selecting one
of its neighbors that is closest to the destination. Each node
has the location information of all its neighbors through
neighbor discovery process. In the neighbor discovery process, each node periodically broadcasts its location information to its neighbor using a MAC level broadcast including
its own identifier. When a neighboring stationary node hears
a beacon, it generates its beacon just once to inform its location to the newcomer. This is called adaptive beaconing.
This allows each node to keep track of their neighbor’s location even during movement while suppressing the unnecessary beacon transmissions. Location information of master
sink while on movement is propagated throughout the sensor field by periodic flooding.
The Elastic Routing Protocol proposed by Yu et al. [124]
is a novel geographic routing scheme for mobile sinks in
wireless sensor network.
The sink sends periodic beacon messages informing its
location to its neighboring nodes and also informs its current
location to the node from which it received the last packet
by greedy forwarding. The source obtains the location of the
mobile sink and forwards continuous data packets to the sink
by greedy forwarding.
3.2.2 Distance based
In [64], the authors have proposed a distance based routing
protocol called Partial partitioning Avoiding GEographic
Routing in mobile wireless sensor network: a survey
Fig. 6 Routing through a
Master Sink in M-Geocast
Routing—Mobile (PAGER-M). It utilizes the location information of sensor nodes and base station to assign each
sensor node a cost. The cost is calculated based on the sensor’s Euclidean distance of shortest path to the base station.
Greedy forwarding [109] is used to forward a packet to the
base station by the sensor node. If there are concave nodes,
the greedy forwarding fails, which can be overcome by forwarding a packet to a neighbor using the high cost to low
cost rule.
The proposed routing protocol in [46] assumes that all
nodes know their own location through Global Positioning
System (GPS). In the sensor area, grid is constructed and
clusters are formed based on a grid ID. The mobile sink
selects the nearest four cluster heads and designates them
as Agent Cluster Header (ACH). It computes its distance to
ACH using the grid ID. All the ACH’s transmit their location information to the CHs related to them. The sink transmits control packets which contains its location information
to the ACHs alone. When an event occurs, the source constructs announcement packets and transmits it to the cluster head that the source belongs to. The CH sends data in
the direction of the stored ACH. Data transmission is done
through the shortest distance. If a sink moves and ACH is
shifted, the previous ACHs wait till it receives the location
information of a new ACH and then transmits the data.
3.2.3 Predictive distance based
M-LEACH [63] assumes all nodes to be location aware using GPS or other location detect scheme and the sensor
nodes are grouped into clusters. The distance between the
cluster head and node is calculated. Using the predicted distance, clustering and routing is done. The cluster heads are
chosen based on their locations such that they minimize the
total power attenuation. Each node sends its location, velocity and energy level to the base station which is stationary.
The BS selects the cluster head. The cluster head broadcasts
Advertisement messages using Carrier Sense multiple Access (CSMA) MAC protocol (Fig. 7) [63]. Cost of node m
to join cluster j
Cma = Wij × dmi
(5)
where Wij —willingness to accept a new node into the cluster; dmi —distance between node m and cluster head i.
A node will choose the cluster with the smallest Cma to
join. A node calculates it’s next allocated time slots based
on the number of nodes in the cluster.
The Receiver based Opportunistic Forwarding protocol
(ROF) [68] allows the neighbor nodes to forward the data.
It chooses the node with the smallest distance to sink, extra coverage area and surplus residual energy to forward the
data.
Forwarding priority of a node is calculated as
{|d − di |/R} × {Ei /E0 } × {ri /R}, d − di > 0;
Pri =
0,
d − di ≤ 0
(6)
where d—distance between source and sink; di & ri —
distance of node i to the sink & source respectively; Ei —
residual energy of node i; E0 —initial energy of node i; R—
node’s communication distance.
The proposed scheme in [59] is a location based energy efficient intersection routing protocol. The source node
makes a limited routing space and transmits the advertisement message. The neighboring nodes calculate d1 and d2
and transmit a reply message only if d1 and d2 are less than
G.S Sara, D. Sridharan
Fig. 7 M-LEACH
its routing space r. The source node gives priority to nodes
of high energy level and short Pn (minimum distance between node A and sink node S).
3.3 Discussion
The topology control decisions are usually based on the
link state information of a node and its neighbors. Inapt
topologies diminish the network capacity by limiting the
spatial reuse of communication channels and reduce the network robustness. The topology control preserves the network power by exploiting the spatial orientation of network
nodes. It provides a good management over network assets
such as battery power and minimizes the redundancy in network communication. It utilizes either the global topology
information or partial link state information for topology
adaptation decisions. This requires that the route information has to be refreshed and updated often.
It is observed from the simulation results [23] that centralized topology control algorithms take longer time to converge to an optimum topology. The distributed topology
control algorithms just maintain the local view of topology
and link state information. They thereby reduce the bottle
neck of the centralized controller and the overheads for distributing the link state information to the controller [23]. It
is also analyzed that topology control routing protocols guzzle a substantial amount of bandwidth. It is opined that the
proactive routing protocols show a remarkable performance
under high traffic load and variable traffic pattern. They permit the reduction of latency time at the cost of energy.
Compared to the reactive routing protocols, the proactive routing protocols have 50 % lesser latency per packet
as shown by the simulation study of Tai-jung et al. [110].
But when the mobility of nodes is rapid, their performance
degrades due to high energy depletion leading to breakage
in the network connectivity. Reactive routing protocol performs well under low or medium mobility of nodes and continuous traffic load. If a fluctuating traffic which produces
higher route discovery constitutes the network, then the reactive routing protocols lose their advantage over proactive
routing.
A good topology control algorithm should address issues like topology control overheads, latency, scheduling
and mobility. Most of these algorithms do not include the
node mobility for topology adaptation. Future work can be
focused towards attaining the knowledge about the node
movement pattern which would render better stability and
adaptivity to the network.
Location based approach stays close to the perfect packet
delivery of 100 % for all distances [38]. These routing protocols are scalable and re-silent to topology changes because
they don’t need route discovery and maintenance but periodic beaconing to update the location creates a lot of congestion. As long as the location information of the destination is valid, the delay incurred by the location based routing
protocol remains stable even under high node mobility.
Table 2 summarizes the various features of Location
based Routing Protocols in MWSN. Restricted directional
flooding to update a node’s location can be used for applications that require high reliability and fast message delivery. But this may not be an ideal solution if the network is
large as multiple copies of each packet are broadcast at the
same time. The inclusion of certain rules as in opportunistic
Routing in mobile wireless sensor network: a survey
Table 2 Categorization of location based routing protocols in MWSN
Protocol
Type
Scalability
Location
updating
Implementation
complexity
Localization
Robustness
Processing
overhead
Power
usage
M-Geocast
Time based
Good
Periodic
Elastic
routing
Time based
Good
Periodic
Moderate
No
High
Less
Low
Less
No
High
Less
(increases if
packet
generation
rate
decreases)
Low
PAGER-M
Distance
based
Good
Event based
Moderate
No
High
Less
Low
ACH
Distance
based
Limited
Event based
High
Yes
Medium
Less
Fair
M-Leach
Predictive
Distance
based
Limited
Event based
High
Yes
Medium
High
Low
ROF
Predictive
Distance
based
Limited
Event based
Moderate
No
High
Medium
Fair
EELIR
Predictive
Distance
based
Good
Event based
Less
No
High
High
Fair
routing protocol to forward location updates like forwarding
location update to prioritized multiple neighbors rather than
simply forwarding it to one hop neighbor nearest to destination is more advantageous.
The Quorum concept [60] can be utilized for location
update services. It helps to re-silence against unreachable
backbone nodes if the number of nodes is large at the intersection of two quorums. The main disadvantage is that the
cost of position update and queries becomes very high if the
quorum sets are large [81]. In location based routing that
utilizes the grid structure to identify the position of node;
strong route maintenance is available even if the grid head
moves because another node from the same grid replaces it
via handoff procedure.
Many of the location based routing [63] use GPS to locate the node’s position. The network infrastructure setup
with GPS is very expensive. Research should devise new location update services which are cost effective to find the
mobile node’s position. Majority of the position based routing protocol consider the nodes as neighbors if the Euclidean
distance between them is equal to the transmission radius.
This is inappropriate to an extent and future research can
take into consideration the irregular transmission radius of
a node due to obstacles and noise, unidirectional links and
different node’s transmission radii [69].
Another major issue is that most of the existing location
based routing methods assume that the location information
of a node is known and never make a mention of how the
locations are known. It would be better if the methodolo-
gies that provide cost effective and energy efficient location
updates are explored further.
4 Classification based on energy efficient routing
techniques
Sensor nodes are energy constrained [115] and have limited
computing power [116]. So it may not be able to run sophisticated routing protocols. The life time of a sensor node
depends mainly on the power supply from a finite battery
source. Stability of a sensor network is proportional to the
longevity of the lifetime of sensor nodes. Therefore the sensor nodes should be able to survive with a small finite source
of energy [48, 49]. It is observed that wireless communication consumes a large portion of the battery power [108].
The transmitter dissipates energy to run the radio electronics and the power amplifier and the receiver dissipates energy to run the radio electronics [119]. Due to the characteristics of random deployment and low cost of sensor nodes,
it becomes difficult and unnecessary to recharge them once
their energies are depleted [61]. Nodes nearer to the sink
deplete their energy quickly, thereby making the sink unreachable. Processing overhead and delay at nodes increases
if the hop count increases due to the fact that the packets
have to be buffered at more nodes on the route. This eventually leads to packet drop. Packet drop will cause retransmission which increases energy consumption [88, 126]. Energy wastage occurs when a node receives more than one
G.S Sara, D. Sridharan
packet at the same time due to collision and subsequent retransmission. Overhearing of packets that are destined for
other nodes incurs tremendous amount of energy depletion.
The control packet overhead adds to increased transmission
leading to energy expenditure. Idle listening and over emitting increases energy wastage [41]. All these factors necessitate the formation of innovative routing techniques to eliminate energy inefficiencies and reduce energy consumption.
Therefore energy consumption presents a major challenge
for routing protocols and algorithms.
4.1 Energy aware routing
Minimum active communication energy can be achieved either by transmission power control or load distribution. The
energy efficient routing protocols that try to reduce the transreceiving energy of a node by adjusting its radio power just
enough to reach the receiving node, avoid routing of packets
through nodes with low residual energy and optimize flooding of routing information over the network are called the
energy aware routing protocols.
4.1.1 Transmission power control
Kihun Kim et al. [59] have proposed an energy efficient routing protocol that minimizes the transmission power by using
location information and the energy levels of sensor nodes.
The source node knows the location of the sink. It transmits an advertisement message to the neighbor nodes. The
neighbor nodes calculate whether to reply or not for the received advertisement message. If it replies, then it includes
its residual energy level information and distance to sink.
On receiving reply from all neighboring nodes, the source
node gives priority to node of high energy level and shorter
distance to sink. These nodes require minimum transmission
power to forward the data. This protocol also performs better than other flooding based routing protocol and location
aided routing as it collects information about neighbors only
when nodes need route discovery; thereby reducing the network overhead and energy consumption.
It is pragmatic from the work of Andrea et al. [2] that
the transmission energy consumption is minimized by triggering STATE UPDATE messages only when needed. It exhibits only a slight increase in energy consumption under
harsh conditions because it minimizes the required traffic to
reliably track the sink by matching the frequency of STATE
UPDATE transmissions to the actual movement pattern of
the mobile station.
The simulations done by Yu et al. [124] reveal that the
elastic routing exploits the overhearing feature of wireless
transmission for location propagation. The node extracts the
sink location information in an overheard packet before discarding it and thereby minimizes the transmission of packets
for location propagation. The results show that this extraction does not consume significant energy as compared to the
overhead of other protocols for location update of a mobile
sink. The link availability based QOS aware routing protocol utilizes the energy consumption estimate to make energy
efficient routing decisions [85].
The power consumption at each hop is written as
Ei (Ti ) = εr BTi + εt BTi + εd din BTi + εi (1 − 2Ti )B
(7)
for i = 1, 2, . . . , k.
εr , εi , εt and εd —coefficients of energy consumption in
receiving, idle, transmitting, dissipation respectively; n—
path loss component; B—bit rate of wireless channel; Ti —
traffic on link between hops i and i + 1.
If the hop distance di is chosen as a constant, then Ei
becomes a constant for a given traffic.
Therefore total energy spent on the route with a fixed distance of d is
k
Ei = kEi
(8)
i=1
This means that the total energy as a routing metric is
equivalent to hop count. This value is embedded in each
node along a path into the route request. It is analyzed from
the simulation that link breakages are reduced to about 25 %
as compared to AODV for high mobility nodes and thereby
they avoid energy loss due to retransmissions.
4.1.2 Load balanced routing
Xiaoxia Huang et al. [117] have proved that cooperative
routing using guard nodes consume lesser energy than non
cooperative routing.
Expected energy consumption for successful delivery
from node i − 1 to i via cooperation,
E = Er × (Ne + 1)(1 − p) / 1 − p Ne +1
(9)
+ Et / 1 − p Ne +1
Expected energy consumption for successful delivery from
node i − 1 to i via non cooperative routing
En = Er (Ne + 1) + Et /(1 − p)
(10)
Since 0 < p < 1,
Et / 1 − p Ne +1 ≤ Et /(1 − p);
Er × (Ne + 1)(1 − p) / 1 − p Ne +1 ≤ Er (Ne + 1)
(11)
Therefore;
E ≤ En
(12)
where Et —energy consumption in transmission; Er —
energy consumption in reception; Ne —neighbor nodes; p—
link error probability.
Routing in mobile wireless sensor network: a survey
In situations where multiple nodes with the same packet
attempt to deliver it to other nodes, cooperative robust routing [99] is used The energy consumption per bit of robust
routing is lower than AOMDV [76] at relatively low mobility.
The CEER algorithm [110] utilizes the cluster head to
aggregate the data and adopts the dynamic path finding to
find a neighbor node with maximum energy to route the data
to a nearby anchor. Energy Saving Dynamic Source Routing
(ESDSR) uses local broadcast to find a path and chooses the
best route with maximum expected life but the source node
does not aggregate data of its neighbor node to send data to
the destination. So CEER algorithm saves more energy as
compared to ESDSR [87] as the network density increases.
The receiver based opportunistic forwarding protocol
[68] considers the residual energy of nodes into the calculation of forwarding priority. It tries to forward data via nodes
that have higher residual energies. This balances the network
energy consumption and improves the forwarding reliability.
The CAEE routing protocol [77] uses the in-network storage
which utilizes the sleeping nodes located near the routing
nodes to buffer the data. It is observed that the increase in
mini sinks (sleeping nodes) avoids congestion and balances
the energy consumption.
Seong-Yong Choi et al. [102] have introduced a Power
Aware Heuristic (PAH) algorithm to achieve robust energy
efficient dynamic routing for a mobile sink in a multihop
sensor network. PAH uses the Maximum Move Hop Count
(MMHC) and Average Residual Energy (ARES) around the
sink. It is seen through simulation that the sink’s movement
reduced workload on the nodes within the energy hole and
so energy consumption was even among the nodes.
4.2 Energy conserving routing protocol
The inactivity energy consumed by a sensor node can be reduced by switching the node’s mode of operation into sleep/
power down mode or by simply turning it off when there is
no data transmission or reception. The routing protocols that
try to minimize this inactivity energy due to idle listening,
overhearing etc., are the power saving routing protocols. Yet
another efficient method to save energy consumption is to
design the sensor nodes or sensor network setup such that
they are able to save energy owing to their hardware design [26]. For example, by recharging node’s battery using
solar energy.
4.2.1 Power save approach
Majid Nabi et al. [78] use the gossiping strategy [24] as
a routing protocol for multi hop data transmission to the
nearest sink node. An efficient MAC layer which reduces
delay and saves energy is used along with the gossiping
process. The nodes store data from the other nodes in the
cache. It forwards a few data which are randomly chosen
from the cache along with its own sensed data and propagates the packet to its neighbors. The Mobile Cluster MAC
(MCMAC) protocol dedicates a part of the active slots to the
mobile clusters (MCS) and the other part to the static nodes
in the network. Each slot is assigned to only one node in the
cluster. If there are many clusters they share the MCS part
using hybrid contention based and schedule based channel
access mechanism. The simulation results show that there is
improvement with respect to existing protocols in terms of
application level latency and reliability as the optimization
methods use the application level QoS metrics of the network to decrease the power consumption overhead without
worsening latency.
Bashir and Jalel [4] proposed an adaptive Mobility aware
and Energy efficient MAC (MEMAC) protocol along with
Dynamic Source Routing. The mobility prediction algorithm utilizes the first order autoregressive model (AR-1)
[127] to predict the current mobility state of a node from its
previous mobility state. It partitions the network into clusters which are dynamically formed. The frames are handled
during multiple phases using hybrid scheme of TDMA and
CSMA. The frame length is adjusted based on the mobility
information of sensor nodes and the number of nodes that
have data to send. This avoids wasting slots by excluding
nodes that are expected to leave or join the cluster and those
nodes that have no data to send using TDMA schedule.
4.2.2 Energy efficient design
The M-Geocast protocol [75] is designed in such a way that
when there are multiple sinks, only one sink is selected as
master sink. The master sink alone periodically updates its
location information. The other sinks only intimate the master sink about their location. All the nodes send their data to
master sink and it in turn forwards the data to the other sinks.
So M-Geocast consumes less energy when the number of
sinks are increased as compared to AODV and Geocast. This
is due to the fact that it requires location update from a single master sink while AODV and Geocast requires substantial overhead due to RREQ flooding and location broadcast
from multiple sinks respectively.
The GBEER algorithm [60] constructs a permanent grid
structure using global location information after the sensor
nodes are deployed. Data requests and reply are sent to the
source and the sink respectively along the grid. This makes
the communication overhead caused by the sink’s mobility
to be limited to the grid cell. No additional energy consumption occurs here due to multiple events because only one grid
structure is built independent of the event. This consumes
less energy than TTDD [20] because in TTDD the grid structure varies in proportion to the number of sources. GBEER’s
G.S Sara, D. Sridharan
energy consumption is not affected by the speed of the sink.
In [31], the sink periodically sends a beacon packet that goes
through longer distance with higher power. Only a few beacon packets are sent here, which saves much energy. The
transmission power of each node is reduced as the beacon
packet from the sink consists of the information needed for
routing.
In the Shortest path routing protocol [130], the F nodes
are designed to have very powerful batteries than the S
nodes. F nodes maintain the routing table, receive and fuse
the data from S nodes and transmit data to C nodes via the
shortest path. The S nodes which are mobile with lesser battery capacity are not stressed here. Power control is used to
invert the power loss. On simulation it is observed that the
average residual energy of S nodes remains more than 90 %
of their total energy as the S nodes only collect data and
transmit them to the fusion node within one hop; thereby
improving the total network’s energy.
4.3 Discussion
The topical research activities (Table 3) on wireless sensor
network are towards the reduction of energy consumption
by sensor nodes. Mobility based energy efficient routing is
a relatively new field of study that is posing a lot of challenges. The mobile node generates a large number of overheads to broadcast its location updates to the other nodes.
This increases the overall energy consumption of the network. Most of the energy aware routing protocol tries to reduce the transmission power required by a node by reducing
the overhead messages. Instead of broadcasting these control packets to all nodes at all time, on demand broadcasting
seems ideal [20, 59]. Nevertheless, the on demand routing
protocols use control packets like RREQ, RREP etc. to discover a path. For an energy aware routing, many solutions
are proposed which utilize the transmission power as a metric to find an energy efficient path. However, it is shown by
Cao et al. [9] that if energy drain rate is considered as a metric rather than transmission power or if min-max algorithms
are used, then better energy efficiency can be obtained. Enhanced energy efficiency can also be obtained by distributing the load among different nodes [77] or by the usage of
backup routes which leads to lesser packet loss and robustness against mobility and fading but there are some open
issues to be considered and successfully addressed for load
balancing to be implemented in real deployment of mobile
wireless sensor network. The increase in total overhead and
packet disorder has to be minimized to reduce the complexity of the routing protocol.
The simulation study in [32] shows that in a system of
N nodes, cooperation with symmetric power allocation can
reduce delay by a factor of 1/C, where C is the total power
budget for the system. The node’s power can also be saved
by turning off the radio when not in use. Yet introducing
the sleep schedule to a node poses a great challenge to data
routing as synchronization between the sender and receiver
becomes a must. The sleeping node can miss the communication opportunity which will result in longer delivery delay and lower energy efficiency [32]. Scheduled communication scheme becomes a very complex task with random
mobile nodes having imperfect clocks. The decentralized
fashion of nodes’ wake up and sleep time is another solution
for achieving power hoard as it is more scalable and easier to implement but the delay incurred by these power save
methods lead to heavy congestion and packet loss. A hybrid method of applying scheduled sleep time for nodes with
low mobility and less congestion in addition to unscheduled
sleep time for nodes with high mobility offers a better power
save approach.
It is analyzed that cross layer mechanism with a cooperation between MAC and network layer when incorporated
with the routing helps to achieve better energy efficiency.
Further research can be focused towards applying reinforcement learning methods to achieve good energy aware routing decision and by designing the network in such a way that
load can be balanced energy efficiently with lesser overhead.
The design of mobile sensor nodes can also be done in such
a way as to utilize nature’s power to recharge their batteries.
5 Classification based on mobility of nodes
Multihop paths are traversed by packets from the sensor
nodes to reach the sink [28]. The nodes closer to the sink are
burdened with more packets to be relayed leading to early
energy depletion in static WSN. Adjusting protocol parameters such as coding rates or initiating new routes along the
existing topology may not allow the network to meet the new
traffic [1]. Mobility has been found beneficial to replenish
energy resources [96] and also to reallocate resources [22].
Mobile nodes can change their location on sensing that the
neighboring node’s energy is depleted. This helps to avoid
link errors, contention overhead and forwarding during routing. Using mobile sensor nodes, shorter hop by hop data delivery can be achieved [123]. This helps to reduce the probability of error which increases with increasing number of
hops that a data packet has to travel. The real challenge of
network routing in MWSN occurs due to the fact that it is
not easy to grasp the whole network topology which keeps
on changing dynamically.
5.1 Routing only when the sink is mobile
Because of the mobility of the sink, the set of sensors located
near the sink changes over time. This does not stress the
sensor nodes that are closer to sink, thereby balancing the
Routing in mobile wireless sensor network: a survey
Table 3 Comparison of energy efficient routing protocols in MWSN
Protocol
Category
Energy consumption
rate
Energy reserve
Network
lifetime
End to end
delay
Collision
avoidance
mechanism
Delivery
ratio
Data Aggregation
MPR
TPC
Very low
Satisfactory
Good
Less
CSMA
based on
BEB
High
No
Elastic
routing
TPC
Low
Satisfactory
Very good
Less
Overhearing
High
No
EELIR
TPC
Low
Supplementary
Good
Moderate
(decrease
with time)
–
High
initially &
drops
eventually
No
REDM
TPC
Medium
Supplementary
Very good
Less
–
LABQ
TPC
Low
Satisfactory
Good
Less
RRP
LB
Low
(increases
as node
mobility
increases)
Satisfactory
Good
Moderate
CEER
LB
Low
Satisfactory
Good (bad
if network
area is
small)
ROF
LB
Low
(increases
as node
density
increases)
Supplementary
CAEE
LB
Low (if no.
of sinks is
more)
MCMAC
PS
MEMAC
MGeocast
Medium
No
–
No
Modified
IEEE
802.11 with
RTS/CTS
Very high
No
Less
CSMA/CA
–
Yes
Good
Less
Dual
channel
based
forwarding
right
contention
mechanism
High
No
Supplementary
Very good
–
In network
storage
–
Yes
Medium
Supplementary
Good
Less
TDMA +
CSMA
High
No
PS
Low
Supplementary
Good
Moderate &
Constant
TDMA +
CSMA
High
No
EED
Low
Satisfactory
Good
Less
–
High but
degrades
with node
speed
No
GBEER
EED
Low
Satisfactory
Good
–
–
Moderate
Yes
SP
EED +TPC
Low
Satisfactory
Very good
–
–
–
Yes
TPC—Transmission Power Control; LB—Load Balanced; PS—Power Save; EED—Energy Efficient Design
energy consumption and prolonging the network life time.
When the sinks are moving, they usually monitor the routing
process with the sources. If the sinks have moved after they
asked data, they will not be able to solve the routing issues
themselves. In these cases, the data should be transmitted
via the relay sensor nodes or other neighboring sink nodes
[46, 80].
Sink can follow three types of mobility patterns in
MWSN [77].
5.1.1 Random mobility
In this case the sink follows a random path in the sensor field
and implements a pull strategy for data collection from the
sensor nodes. Data can be requested from either one hop or
k hop neighbors of the sink [77].
The Elastic routing [124] assumes all sensors to be static
except the sinks which can move freely in the network. Each
node obtains its location information via GPS or other location services [52, 113, 125]. The mobile sink sends beacon
G.S Sara, D. Sridharan
messages to announce its current location to neighbor sensor nodes. Each node searches its neighbor list for the sink
before forwarding the data packet. The packet is forwarded
directly to the sink without further calculation if the sink is
available in the neighbor’s list [106]. On movement, the sink
checks if it has moved out of range of the last forwarding
node. In that case it informs its current location to the last
hop forwarding node by unicasting. The other nodes overhear this transmission and reset the location information of
the sink in the received data packet to the new location.
Andrea et al. [2] have considered a sensor network scenario where a set of static nodes {si } with known geographic
positions, a mobile sink that moves with time varying speed
v(t) along an unknown trajectory r(t) through the sensor
field and a source S that is located at a known position
are deployed. The Mobility Prediction Routing algorithm
is used here. The mobile sink transmits a STATE UPDATE
message containing the current estimate of its state following the standard 802.11 CSMA mechanism based on Binary
Exponential Backoff (BEB) [40]. The basic geographic routing is applied for further communications. Ioannis Chatgigiannakis et al. [43] have used the random walk mobility
model for the sink to collect data from the sensors. They
have demonstrated that by using sink’s random mobility, the
energy spent in relaying traffic is reduced and the network
lifetime is extended.
In the Grid Based Energy Efficient Routing [60], the mobile sinks have a random trajectory. The sensor node generates data based on an event and sends it to the header. The
header sends the data announcement packet to other headers. When the mobile sink which is moving randomly needs
the data, it sends data request to the nearest header via local
flooding. The data request packet is then forwarded to the
source’s header which then transmits data to the sink.
5.1.2 Predictable/fixed path mobility
The mobility trajectory of sink here is along a known fixed
path. CAEE routing protocol [77] utilizes the discrete sink
mobility along a fixed path in the sensor network. The data
collector (DC) node collects and stores the data from the
sensors in the mini sink. The mobile sink periodically visits
the minisinks and collects the data from the DC node. The
mobility path of the sink is along the periphery of the sensor
node. Jun et al. [56] has described a scheme that is based
on discrete mobility of the sink where the sink’s pause time
is greater than its mobility time in the sensor field. They
have shown that longest lifetime for the sensor network can
be achieved if the mobile trajectory of the sink is along the
periphery of the sensor field. Their results also show that
a better routing strategy is to use a combination of round
routes and short paths.
Branislav et al. [6] have used a sensor node closer to mobile sink as the relay node. The relay node acts as the data
sink for all the traffic to the mobile sink. Routes are setup
using the gradient of information potentials. The mobility
graph is extracted from the radio signal strength (RSSI)
traces of users in the environment. The future relay nodes
are predicted with the help of mobility graph. The routing algorithm updates information potentials for both the current
and predicted relay node, guaranteeing that new information
potential is ready once it is needed.
5.1.3 Controlled mobility of sink
The main challenge in controlled mobility is to design sensor network protocols that can exploit mobile components
effectively and solve the navigational problems for mobile
elements [1]. Stefano Basagni et al. [107] define a Mixed
Integer linear Programming (MILP) analytical model whose
solution determines those sink routes that maximize network
lifetime. The Greedy Maximum Residual Energy (GMRE)
heuristic is used to move the sink to a new location with
highest residual energy. The sink greedily selects the site
within dMAX surrounded by nodes that have the maximum
energy left. After spending a time t minutes at a site, the sink
evaluates whether to move to its adjacent site. Two sites are
adjacent if their distance ≤ dMAX . It evaluates by calculating
the residual energy at nodes around each of potential future
sites and compares with the residual energy at the current
site. Sink moves to the site with the highest residual energy
by querying the sentinel. The sentinel sensor node measures
the residual energy at a site by flooding.
In the Reactive Sink Mobility algorithm [14], the sink
moves opportunistically based on a form of feedback from
the network. The sink is allowed to move around within an
area of the order of a square wave length. A gradient based
reactive routing protocol along with connectivity discovery
process is implemented. The experimental evidence demonstrates that limited sink mobility increases the fault tolerance
of a sensor network and enhances the existing load balancing properties. It increases load balancing by using reliability feedback. The authors [66] have proposed a routing protocol that utilizes the sink’s mobility along with the existing
routing protocol IPV6 Routing Protocol for Low Power &
Noisy Network (RPL). It is a distributed and weighted strategy that improves the network lifetime by moving the sinks
towards the leaf nodes. The sinks are moved based on three
parameters namely—energy (ei ), number of hops (hki ) and
number of neighbors (bi ).
Weight Wi = βhki × ei + γ bi
(13)
β and γ are coefficients of normalization. By moving the
sinks according to the weighted approach, nodes playing the
relay nodes change and the data packets become more reliable.
Routing in mobile wireless sensor network: a survey
5.2 All nodes are mobile
The variant of geometric routing protocol called M-Geocast
[75] considers the case of fully mobile sensor network where
any node can move anytime. It designates one of the sinks
as master sink which acts as a location service provider and
data collection and dissemination server. It utilizes simple
geographic forwarding to send messages to master sink. It is
pragmatic from the simulation results that M-Geocast’s delay remains stable even as the node’s speed increases due to
the fact that no additional overhead is incurred due to mobility as long as location information of the destination remains valid. It is observed that M-Geocast consistently delivers more than 96 % of all the events for most of the cases.
The routing hole which occurs in most geometric routing
scheme is reduced here.
Chang et al. [110] have proposed a routing algorithm
where all the nodes are mobile. A node on arriving at a new
location sends an anchor information request to neighbor
nodes. If a neighbor node has the anchor’s RGB values, it
transmits the information to the new node. The new node
calculates and selects the smallest Dik value to the Kth anchor. Then it updates its RGB values and transmits it to the
server. The position of the node i will then be updated in
the location database. This algorithm avoids topology-hole
problem. It efficiently chooses a better routing path with energy awareness.
Zou et al. [64] have designed a routing protocol for a sensor network where all the sensor nodes move randomly with
random velocities within the sensing field. A cost function
which has a value close to the Euclidean length of the shortest path to the base station is assigned to each sensor node.
Greedy forwarding is used to forward a packet and when a
packet reaches sensor nodes near local minimums, the high
cost to low cost rule is applied. A sensor node is provided
with multiple forwarding candidates to reduce transmission
failure. The beacon interval is prolonged and randomized to
reduce the interference and routing overhead.
The Robust Cooperative routing protocol [117] considers
a network scenario where all the nodes are mobile. It provides robustness against node mobility. A node learns the
partial path information by overhearing ongoing transmission. If it hears transmissions correctly from two intended
nodes that belong to the same flow, indicated by source and
destination, it becomes the guard node. Through cooperative
routing a new path is set up with small overhead when many
nodes move away. This protocol chooses the best path by
utilizing path diversity in robust path.
sink. It broadcasts the queries as it moves. Each node within
its range finds the shortest distance to the microserver from
the various queries it has received. The nodes respond to
the query that has arrived via the shortest path. This helps
to achieve sustainability of the network by reducing relay
overheads.
Shah et al. [103, 104] analyzes three tier architecture of
sensor network (Fig. 8) comprising of a top tier of WAN
connected devices, a middle tier of mobile transport agents
called as MULES and a bottom tier made up of fixed wireless sensor nodes. The top tier acts as the access point. The
movement of the MULE is random.
The MULES upload the data from the sensor nodes and
carry it to the access point.
They have large storage capacities, renewable power and
ability to communicate with the sensors, other MULES and
access points. The static sensor nodes communicate using
short range radio. The key advantages of MULE architecture are robustness and scalability. They increase reliability
by acting as the redundant access point and create a fault
tolerant design.
Venkitasubramaniam et al. [111] have considered a network where n sensors communicate to a mobile Access
point (AP) over a common channel. The networking functions of the sensors are shifted to a set of interconnected
super nodes which act as the mobile access point here.
They are observed to have lesser power and bandwidth constraints. The opportunistic ALOHA that uses channel state
information in conjunction with orthogonal code division
multiple access is utilized by mobile AP.
5.4 Few nodes are stationary
Luca Borsani et al. [74] have proposed HAT mobile protocol where the nodes are divided into two categories namely
fixed and mobile nodes. Fixed nodes create the routing tree
and mobile nodes join the routing tree as leaves. This protocol limits the impact of signaling overhead required to support the handover procedure thereby reducing energy consumption due to network mobility. It also reduces packet error rate.
The authors in [130] utilize the S nodes with limited storage and random mobility to collect data. The F nodes are
stationary and they receive and fuse the data from S nodes
and send it to the C nodes which are data ware house of
multilayer mobile WSN.
5.3 Few nodes act as relay nodes
5.5 Discussion
Aman et al. [1] proposed a protocol where a mobile microserver which acts as relay node, moves across the network to route data from static sensor nodes to the sink. Initially the microserver transmits the queries on behalf of the
Table 4 compares the different routing protocols based on
the type of mobility imbibed. In a static sensor network, the
nodes with the best channel to the base station have a heavier
load than their peers due to relay traffic. This reduces their
G.S Sara, D. Sridharan
Fig. 8 Mobile wireless sensor
network with mobile relays
network lifetime leading to hot spot problem [14]. Imbibing
mobility in sensor network helps to alleviate this problem at
the cost of finding an efficient route. It is demonstrated by
experiments that the traffic experienced by the most heavily
loaded node is reduced by a factor of three with an arbitrary
mobile strategy [56]. The routing protocol should be pliable
to the self organizing nature of the nodes. Random mobility
provides improved data capacity [17, 30] and networking
performance [18, 55, 103]. But the latency of data transfer
cannot be bounded deterministically and delivery itself can
be in jeopardy if the data is cleared from the buffer [1].
It is observed that if the mobility of some nodes can be
controlled, then these nodes can be moved to optimize energy efficiency of the network [6, 16, 39, 112] or else if the
mobility can only be predicted but not controlled, then this
mobility can be utilized to transport data [6, 47, 50]. It is
pragmatic that controlled mobility of the sink improves the
network lifetime up to six times when compared with the
static sink and up to two times on comparing with the random sink mobility [107]. Controlled mobility also helps to
achieve power efficiency of the network [19]. Jun et al. [56]
has suggested that peripheral movement of the sink helps in
better routing of data. On using a mobile agent it is observed
that they can move closer to the node to collect data and forward it to the sink. This helps in conserving energy since
data is transmitted over fewer hops thus reducing the number
of transmitting packets. By reducing the number of hops, the
probability of transmission error and also collision reduces.
The sensor nodes can reduce their transmission range to the
lowest value required to reach the mobile infrastructure [43].
Routing efficiency is improved when the sink velocity
is increased since in unit interval of time, the mobile sink
can meet more sensors and gather more information. But
if the mobility speed is high, the sink may not be able to
collect a potentially long packet. During sink movement it
becomes important to reduce the traffic required to reliably
track the sink in order to reduce the energy spent [2]. If the
sink moves with a relatively higher speed, the data packets
delivered to the sink will take a flexural path as seen in Elastic routing [124] leading to higher packet delivery delay.
It is observed that if the location information of the nodes
is known, then it will not affect the routing much even if the
node’s speed increases as not much additional overhead is
incurred [75].
Future work should focus on issues like timely and
energy efficient discovery of mobile nodes, transmission
scheduling, finding optimum value of mobile node velocity,
reduction of traffic to track the mobile node, data transfer
between mobile node and static sensor node.
6 Biologically cooperative routing
Nature inspired routing protocols provide remarkable adaptation, reliability and robustness in various environments,
even under hostility. The swarm intelligence concept imbibed in the cooperative routing reduces the control traffic,
the complexity of an individual node and increases the robustness to changes in network topology [13]. Ant colony
Routing in mobile wireless sensor network: a survey
Table 4 Comparison of routing protocols based on mobility
Scheme
Mobile
node
Mobility
pattern
Sink
movement
Location
tracking
Mobile
node
speed
Network
density
Routing
F. Yu
[124]
Sink
Random
Random
path
GPS
Variable
High
Elastic
Andrea
Munari [2]
Sink
Random
Random
path
Mobility
Prediction
algorithm
Adaptive
Medium
Mobility
prediction
Kisuk
Kweon
[60]
Sink
Random
Random
path
Quorum
Variable
Low
Grid based
Majid I
[77]
Sink
Predictable
Peripheral
path
Mini sink
Constant
High
Collision
avoidance &
energy
efficient
Branislav
Kusi [6]
Sink
Predictable
Predicted
path
Mobility
graph based
on RSSI
Constant
Medium
Information
potential
based
Stefano
Basagni
[107]
Sink
Controlled
Predicted
path
Greedy
Maximum
Residual
energy
approach
Constant
Medium
Shortest
path with
GMRE
Daniele
Puccinelli
[14]
Sink
Controlled
Predicted
path
Connectivity
Discovery
process
Adaptive
Low
Gradient
based
Leila Ben
Saad [66]
Sink
Controlled
Predicted
path
Weighted
approach
Variable
Medium
IPv6 routing
Lynn Choi
[75]
All nodes
Random
Random
path
Master sink
Variable
Low
Geometric
routing
Le Zou
[64]
All nodes
Random
Random
path
Euclidean
length
Variable
Low
Greedy
forwarding
Xiaoxia
Huang
[117]
All nodes
Random
Random
path
Overhearing
feature
Variable
Low
Cooperative
Aman
Kansal [1]
Relay
node
Random
NA
Querying
Controlled
Low
Shortest
path
R.C. Shah
[103]
Relay
node
Random
NA
NA
Variable
Low
Mule
Zhi Feng
Duan
[130]
Sensor
node
Random
Fixed path
Fusion node
Variable
Medium
Shortest
path
Lucca
Borsani
[74]
Sensor
node
Random
NA
Handover
technique
and routing
table data
constant
Medium
Tree based
optimization technique, insect population inspired wireless
sensor network, bee colony algorithm, cellular automata and
genetic algorithms can be utilized to create optimum routing
algorithm for mobile wireless sensor network. ACO algorithms are a kind of metaheuristic search algorithm. It uses
the swarm intelligence concept [98].
GPS/Ant like routing algorithm (GPSAL) based on GPS
and mobile software agents initiates the ant’s behavior for
routing in a mobile network. The route discovery is accelerated using mobile software agents modeled as ants which
are responsible for collecting and spreading updates about
the data location information of the mobile hosts. Ants communicate with each other by using the stigmergy technique.
Stigmergy means the indirect communication of concerned
individuals through changing environment. In on demand
routing algorithm (ARA) [84], the route discovery is per-
G.S Sara, D. Sridharan
formed by flooding forward ants to the destination as well as
establishing the reverse links to the source. This is similar to
Dynamic Source Routing (DSR). The routes’ maintenance
in ARA does not need any other extra particular messages
during transmission. Pheromone operating rule is given by,
ϕijk (t)
(14)
ϕj (t) = (1 − ρ)ϕij (t) +
where ϕj (t)—pheromone trail from node i to j ; ρ—decay
factor; ϕijk (t)—quantity of pheromone laid on edge (i, j )
by ant k.
The integration of ant based routing and AODV routing
protocol [84] enhances the node connectivity and decreases
the end to end delay as well as route discovery latency. The
deployment of ants in AODV increases the node connectivity and reduces the amount of route discovery.
A Tracking Range Based ant Colony routing protocol
(TRAC) [121] uses the tracking range of mobile sensor
nodes to split the search path into two parts: indefinite path
and definite path. The message from base node is first send
probabilistically through indefinite path until the tracking
range of mobile destination sensor node is reached. Then
the message is sent through the definite path in the tracking range. With the mobility of the mobile destination node,
the length of the indefinite path is decreased, thus reducing the entire path traveled by the artificial ants. Termite is
a routing protocol that is based on the principles of swarm
intelligence [82]. As packets are dispatched from a source
to a destination, each packet follows a preference (bias) towards its destination while the packet will follow the updated preference back to its source. The bias is known as the
pheromone. In termite routing, an exponential decay equation for pheromone is adopted as
ϕij (t) = e−t ϕij (t) + ϕij (t)
(15)
where t is elapsed time; ϕij (t)—pheromone in network
memory; ϕij (t)—current update.
Authors in [98] have proposed a parallel and distributed
reporting cell planning algorithm to locate the mobile terminal to route the incoming calls based on cellular automata.
Cellular Automata (CA) represents a system of distributed,
locally interacting cells that evolve according to a set of
rules. Genetic algorithm is used to discover efficient CA
transition rules. Zhanshan and Axel [129] have considered a
mobile wireless sensor network as analogous to a flying insect population in several aspects. The interaction between
individuals, either insects or WSN nodes can be captured
with evolutionary game theory models in which individuals
are players and reliability is the fitness of each player.
Genetic algorithms (GA) are particular class of evolutionary algorithms which rely on techniques inspired by evolutionary biology such as inheritance, mutation, selection
and recombination. These algorithms are implemented using computer simulations in which a population of abstract
representations of candidate solutions is transformed into an
optimization problem [45, 53]. Ataul Bari et al. [3] have proposed a genetic algorithm based approach for energy efficient routing in two-tiered sensor networks i.e. sensor nodes
and relay nodes with higher power. The relay nodes perform
the routing here. An efficient solution, based on the Genetic
Algorithm for scheduling the data gathering of relay nodes
is used here in conjunction with the routing. Experimental
results clearly demonstrate that this approach significantly
increases the lifetime of the network (by nearly 200 % on
average), compared to traditional routing schemes that do
not consider energy dissipation of the nodes.
6.1 Discussion
“The natural systems through evolution have produced
highly complex systems showing globally coordinated information processing with no global coordination” [98]. The
swarm intelligence concept used in biologically cooperative
routing for mobile wireless sensor network helps to reduce
control traffic by collecting network information from overheard packets. It helps to reduce the complexity of an individual node by modeling all routing functions mathematically. It increases robustness of the system with respect to
changes in network topology due to the emergent routing
behavior from node interactions. By applying ant colony
optimization to routing, fair energy usage can be accentuated by adding the battery information of the node in the
ant packets. The disadvantage of ACO is that it does not
maintain the local connectivity because of which the source
keeps sensing packets even during link failure leading to a
large number of unsuccessful transmissions. Another major
disadvantage of ACO is that if there are insufficient numbers
of routes, then the nodes have to buffer packets till an ant arrives. It is observed that ACO in combination with AODV
increases node connectivity as the probability of receiving
replies quickly from neighboring nodes is high here. Congestion problem is alleviated quite well and a better network
load balance is achieved here as compared to ACO. End to
end delay of packet transmission is mitigated. Connection
establishment time due to processing delay for route discovery is minimized using ACO with AODV.
In Termite routing, nodes maintain knowledge of most of
the destinations. So minimum number of control packets are
needed. Routes are repaired automatically. It is not energy
efficient or bandwidth efficient as large data packets explore
the network. The application of evolutionary game theory to
routing can make the network more reliable and fault tolerant [129]. The cellular automata systems can translate the
global criteria of reporting cells’ problem to local transition
rules of cellular unit. Using genetic algorithm it is possible
to construct heterogeneous networks by allowing the nodes
to transmit at different power levels [45]. Recent research
Routing in mobile wireless sensor network: a survey
activities for GA based routing in static sensor network have
shown a very significant outcome. These works can be further extended to solve the combined problem of mobility
and routing.
7 Conclusion
Routing in a mobile wireless sensor network is one of the
demanding issues of the recent years. Topical research progresses have made a promising evolution in MWSN routing.
In this paper, we have presented a broad survey of the up to
date routing protocols proposed in the literature for MWSN.
We have cataloged them based on their network structure,
state of information, mobility and energy efficiency. These
routing protocols have their own pros and cons and most of
them are application oriented. Based on our observation, we
can suggest that the flat based routing is a suitable choice
for simple network but for a large network the hierarchical routing seems apt. We have also observed that the hybrid
routing protocols that exploit the proactive and reactive routing techniques will provide efficient routing solutions in a
dynamic topology network if the issues related to mobility,
processing overheads and end to end delay are eased. The
location based routing protocols are lucrative but they must
use cost effective methods to perform location updating. We
have also analyzed that the cross layer combination of energy aware routing technique with the application of energy
conserving method will tend to provide a proficient solution
for energy efficient routing in MWSN. The mobility of sensor nodes helps to reduce issues like routing holes, hot spot
problem and energy-hole problem etc. But there is still room
for developing the modus operandi for efficient discovery of
nodes and proper scheduling of transmission. The inclusion
of QoS parameters as routing metrics will help to improvise
the efficacy of the routing algorithm. We perceive that the
biological cooperative routing techniques or nature inspired
routing techniques are emerging as a new area of research
which promises for a more adaptive and robust routing in
near future. Not much of work has been done to ensure secure routing in mobile wireless sensor network. Security issues in routing are another class of approach that will be
drawing the attention of researchers in the coming years.
Acknowledgements This paper is supported by the Junior Research
Fellowship for Engineering and Technology under University Grants
Commission, India. We would like to thank the anonymous reviewers
for their valuable suggestions towards the improvisation of this paper.
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Getsy S Sara received her B.E. degree with distinction in Electronics
& Communication from Bharathiar
University, India in 2004 and M.E
degree with distinction in Digital
Communication and Network Engineering from Anna University, India in 2006. Currently she is pursuing her Ph.D. degree in the Faculty of Information & Communication Engineering, Anna University
Chennai, India. Her research interests include wireless ad hoc networking, sensor networks, energy
efficient routing protocols and communication systems. She is an IEEE student member.
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D. Sridharan received his B. Tech.
degree and M.E. degree in Electronics Engineering from Madras Institute of Technology, Anna University in the years 1991 and 1993 respectively. He got his Ph.D. degree
in the Faculty of Information and
Communication Engineering, Anna
University in 2005. He is currently
working as Associate Professor in
the Department of Electronics and
Communication Engineering, CEG
Campus, Anna University, Chennai,
India. He was awarded the Young
Scientist Research Fellowship by
SERC of Department of Science and Technology, Government of India. His present research interests include Internet Technology, Network Security, Distributed Computing and Wireless Sensor Networks.
He is a life member of Institution of Electronics and Telecommunication Engineers (IETE), Indian Society for Technical Education (ISTE)
and Computer Society of India (CSI).